An estimated 50 to 70 million Americans suffer from sleep and wakefulness disorders and approximately 45% of Americans sleep less or more than the recommended 7-9 hours per night. Both those short and long nightly sleep durations, outside of the 7-9 hours range, are associated with health problems including inflammation, depression and anxiety, diabetes, stress, drug abuse, poor quality of life, obesity, and fatigue related accidents on the job/while driving. However, mechanisms underlying such negative health consequences of short and long habitual sleep durations are poorly defined. Given technological advances in omics-based analyses over the past decade, metabolomics represents a viable approach to characterize thousands of small molecules in plasma thus facilitating detailed characterization of individual metabolic phenotypes. Supporting this, a key outcome of the 2015 NHLBI workshop entitled ?Developing Biomarker Arrays Predicting Sleep and Circadian- Coupled Risks to Health? called for the use of omics approaches to identify markers of long-term sleep behavior. Thus, plasma metabolomics is an attractive approach to identify the impact of habitual short and long sleep durations on biochemical mechanisms linked to disease risk and negative health outcomes. The overall goal of this R21 award is to use existing sleep and plasma metabolomics data within established cohorts to identify metabolite markers of long-term sleep duration and their contribution to subsequent weight gain ? an important chronic disease risk factor. Specifically, we will use existing metabolomics data from three well-characterized cohorts?Women?s Health Initiative, Nurses? Health Study, and Nurses? Health Study II?to identify altered plasma metabolites associated with habitual short and long sleep durations. We expect our findings will advance our understanding of how habitual sleep duration impacts mechanisms and biochemical pathways underlying weight-gain and cardiometabolic disease risk. Furthermore, metabolites altered by either short or long habitual sleep durations can serve as potential biomarkers of overall sleep health, setting the stage for follow-up analyses. Our aims are responsive to key goals of PAR-17-004 ?Secondary Analyses of Existing Datasets in Heart, Lung, and Blood Diseases and Sleep Disorders?, and supports the 2011 NIH Sleep Disorders Research Plan to ?Identify genomic, proteomic, metabolic, and developmental biomarkers of sleep deficiency and biological timing enabling objective assessments of the associated health risks.?